Fatigue failures in vehicle subframes are a critical challenge due to complex and unpredictable loads. Traditional methods often fail to capture the uncertainty in load conditions, resulting in unreliable fatigue life predictions. This study introduces an improved bootstrap method to address these uncertainties. Real-world vehicle testing data were used to construct load spectra with the Generalized Pareto Distribution model, enabling accurate prediction of rare but impactful load events. The rain-flow counting method was used to perform frequency statistics on the load signals. The obtained S-N curve was corrected based on the Haibach theory. This process provided the distribution parameters of the mean and amplitude. Fatigue life was then estimated using a modified S-N curve and Miner’s theory, which achieved significant improvements in prediction accuracy and reliability. This study improves prediction accuracy and can be applied to product design and improvement in mechanical engineering and related fields.
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